臺灣博碩士論文加值系統

近年來由於無線科技的逐漸進步與行動裝置應用上的普及化，企業M化的概念和議題也隨之興盛，企業M化不僅提升企業內部的營運效率，同時成為企業間彼此競爭之重要指標。對行動使用者來說，使用者可不受時空之限制，隨時(Anytime)、隨地(Anywhere)接收服務提供者(Service Provider)所傳送的行動情資(Mobile Message)，相對地，此便捷之方式將形成許多問題，例如連續的發送行動情資，可能導致使用者接收的情資數量供過於求，且未符合使用者需求而淪為不必要的情資，甚至可能造成使用者對系統的反感與不信任感。
為解決上述問題，周裕健學者於過去提出一個以本體論技術建構個人化行動情資之推薦機制(Personalized Mobile-Message Recommendation, PMR)，運用本體論建立情資範例本體論及使用者相關資料。為分析本體論資料間是否相似，以本體論比對(Ontology Matching)技術進行情資類別間的差異性比對，分別設計三種本體論結構相似性比對方式(1)廣度優先比對(Breadth-First-Matching, BFM)、(2)深度優先比對(Dept-First-Matching, DFM)與(3)節點索引比對(Node-Index-Matching, NIM)，並評估其比對效能，經實驗證明，NIM具有較佳的比對效能。針對產生推薦列表的部分，以多數相似門檻值(Most Similarity Threshold, MST)的方式擷取可推薦項目，此門檻值以單純的多數決概念定義，換句話說，使用者對行動情資的瀏覽數量須達情資範例本體論節點總數的一半，故系統無法針對行動情資瀏覽量較低的使用者產生推薦。
為改善上述問題，本研究提出以本體論導入個人化行動情資之成對推薦(Personalized Pair-Recommendation, PPR)，成對推薦之概念來自搭售(Bundling)策略，PPR旨在以搭配之方式推薦行動情資給予使用者，不以銷售為最終目的，除此之外，PPR以個人化為主，分別運用內容導向式推薦(Content-based Recommendation, CB)與協同過濾式推薦(Collaborative Filtering Recommendation, CF)產生成對推薦給予使用者。
透過實驗證明，本研究提出的兩種PPR推薦方法，不論在推薦成功率或F1指標的效能表現，皆優於其它推薦方法，故本研究採用混合式推薦，依據使用者對行動情資的瀏覽特性及傾向，分別使用兩種推薦方法產生推薦。

Recently, due to the progress of wireless technology and the popularity of mobile device applications, the concept and topic of enterprise mobilization are frequently discussed. Enterprise mobilization not only can enhance the internal operation efficiency of an enterprise, but also can be an important indicator for competing with other enterprises in the marketplace.
The mobile user is able to receive the mobile message anytime and anywhere without the restriction of space-time, nevertheless, such a convenient way also brings some problems, for example, in the condition of continuous passing mobile message, the number of mobile messages received by the mobile user will be too much or redundant. In a more serious consequence, it may cause the mobile user’s dissatisfaction and distrust to the system.
The researcher Chou (2009) proposed a Personalized Mobile-Message Recommendation (PMR) module in order to solve the problem that is mentioned above. The PMR system applies ontology to construct the mobile message template and mobile user-relevant information. In order to analyze the similarity between mobile message template and mobile user-relevant information, the ontology matching will be used to compare the difference of the mobile message. This study designs three different ontology structure similarity matching methods, such as (1) Breadth-First-Matching (BFM), (2) Dept-First-Matching (DFM), and (3) Node-Index-Matching (NIM) methods. Among these 3 methods, the NIM has better matching performance. For the recommendation phase, PMR system produces the recommendation list based on Most Similarity Threshold (MST) to extract the appropriate mobile message to the mobile user. MST means the browsing number of the mobile message has to exceed a half of the total number of the mobile message, therefore, PMR module cannot generate recommendation for the user that has less browsing number of mobile message.
In order to improve the problem that is mentioned above, this study proposes a Personalized Pair-Recommendation (PPR) for the mobile message based on ontology approach. The concept of Pair-Recommendation springs from the bundling which is a type of marketing strategy. However, the purpose of pair-recommendation is to recommend related and preferred paired message rather than focus on the sale’s rate. The proposed PPR will analyze the mobile user’s preference according to the personalized ontology of user’s preference. The proper mobile messages are pair-recommended to various users by using the Content-based (CB) method or Collaborative Filtering (CF) method.
According to the results of the experiment, the proposed Personalized Pair-Recommendation (PPR) has better outcome in terms of successful rate (SR) and F1-Measure. Consequently, we adopt the hybrid recommendation method to recommend the mobile message and to achieve the upersonalized recommendation.

Table of Content
摘　要 I
Abstract III
Acknowledgement (誌謝) V
Table of Content VII
List of Tables X
List of Figures XII
Chapter 1　Introduction 1
1.1 Background and Motivation 1
1.2 The Purpose of the Research 6
1.3 Scope 7
1.4 Thesis Organization 8
Chapter 2　Literature Review 10
2.1 Recommendation System 10
2.1.1 Content-based Recommendation 11
2.1.2 Collaborative Filtering Recommendation 13
2.1.3 Hybrid Recommendation 16
2.2 Bundling 17
2.2.1 Types of Bundling 21
2.2.2 Bundling in the RS 22
2.3 Ontology 23
2.3.1 Definition 24
2.3.2 Combinition of Ontology 25
2.3.3 Attribute of Ontology 27
2.3.4 Ontology Application 27
2.4 Personalized Mobile-Message Recommendation (PMR) 30
2.4.1 Mobile Message 30
2.4.2 Structure of PMR 32
2.5 Artificial Neural Network 36
2.5.1 Network type of ANN 37
2.5.2 Adaptive Resonance Theory Network 38
2.6 Performance Evaluation 43
Chapter 3　Research Methodology 45
3.1 Structure of Personalized Pair-Recommendation (PPR) 45
3.2 Ontology Construction Module 48
3.2.1 Message Template Ontology 49
3.2.2 Personalized Preference Ontology 50
3.3 Message Database and User Database 52
3.4 Ontology Matching Module 53
3.5 Personalized Pair-Recommendation Module 54
3.5.1 Content-based Recommendation 59
3.5.2 Collaborative Filtering Recommendation 69
Chapter 4　Experiment 82
4.1 Experimental Data Set 82
4.2 Experimental Tool 87
4.3 Experimental Process and Results 87
4.4 Performance Evaluation 91
4.4.1 Performance Evaluation of CB Recommendation 91
4.4.2 Performance Evaluation of CF Recommendation 93
4.4.3 Performance Comparisons 95
Chapter 5　Conclusions and Future Works 98
5.1 Conclusions 98
5.2 Future Works 99
References 101


List of Tables
Table 2-1 Comparison of CB and CF 15
Table 2-2 Comparison of Hybridization Methods 17
Table 2-3 Comparison of Bundling Definition 19
Table 2-4 Bundling Strategy Applications 20
Table 2-5 Comparison of Bundling Strategy 21
Table 2-6 Research with Bundling Strategy 23
Table 2-7 Comparison for Ontology Definition 25
Table 2-8 Comparison of RS Based on the Ontology 29
Table 2-9 Mobile Message Type 32
Table 2-10 Variable of ART Network 40
Table 2-11 Variable and Definition of SR 44
Table 3-1 Variable and Definition of TCB 60
Table 3-2 Variable and Definition of PCB 64
Table 3-3 Variable and Definition of RCBP 67
Table 3-4 Variable and Definition of TCF 70
Table 3-5 Variable and Definition of PCF 72
Table 3-6 Variable and Definition of CF-1 75
Table 3-7 Variable and Definition of GCFP 77
Table 4-1 Node Amount of Every Layer in the MTO 83
Table 4-2 Data of MTO 83
Table 4-3 Amount of MC for Each Message Type of MU_04 86
Table 4-4 Total Amount of the Node of Each Layer for Users 86
Table 4-5 Experimental Tools 87
Table 4-6 Data of the Users’ PPO 89
Table 4-7 Clustered Result by Using ART (take 6 clusters as example) 91


List of Figures
Figure 1-1 Structure & Procedure of This Research 9
Figure 2-1 Process of Memory-based CF 14
Figure 2-2 Structure of PMR 33
Figure 2-3 Ontology Structure (for example) 34
Figure 2-4 Structure of ART Network (Data Source: [4]) 39
Figure 3-1 PPR Structure 47
Figure 3-2 Message Template Ontology 49
Figure 3-3 Ontology Structure 50
Figure 3-4 PPO Updating Process for a New User 51
Figure 3-5 The PPO of the new user 52
Figure 3-6 Recommendation Process of PPM 54
Figure 3-7 Coding Example of ART Clustering 58
Figure 3-8 Recommendation Example of TCB (User_A) 63
Figure 3-9 Recommendation Example of PCB (User_B) 66
Figure 3-10 Recommendation Example of RCBP (User_C) 69
Figure 3-11 Recommendation Example of TCF (User_A) 71
Figure 3-12 Recommendation Example of PCF (User_B) 74
Figure 3-13 Recommendation Example of CF-1 (User_C) 76
Figure 3-14 Recommendation Example of GCFP (User_D) 80
Figure 4-1 Original MTO Data (partcial example) 84
Figure 4-2 Original PPO Data of the MU_04 85
Figure 4-3 Experimental Process 88
Figure 4-4 The Vigilance Test of Using ART Network 90
Figure 4-5 Comparison of CB Recommendation Performance 92
Figure 4-6 Comparison on SR for the CB Methods 93
Figure 4-7 Comparison of CF Recommendation Performance 94
Figure 4-8 Comparison on SR for the CF Methods 95
Figure 4-9 Performance Comparison on F1 96
Figure 4-10 Performance Comparison on SR 96

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